Search Results (3,597)

In order to meet the needs for the rapid detection of crop growth and support variable management in farmland, an intelligent chlorophyll content in cotton leaves (CCC) detection system based on hyperspectral imaging (HSI) technology was designed and developed. The system includes a near-infrared (NIR) hyperspectral image acquisition module, a spectral extraction module, a main control processor module, a model acceleration module, a display module, and a power module, which are used to achieve rapid and non-destructive detection of chlorophyll content. Firstly, spectral images of cotton canopy leaves during the seedling, budding, and flowering-boll stages were collected, and the dataset was optimized using the first-order differential algorithm (1D) and Savitzky–Golay five-term quadratic smoothing (SG) algorithm. The results showed that SG had better processing performance. Secondly, the sparrow search algorithm optimized backpropagation neural network (SSA-BPNN) and one-dimensional convolutional neural network (1DCNN) algorithms were selected to establish a chlorophyll content detection model. The results showed that the determination coefficients $R_p^2$ of the chlorophyll SG-1DCNN detection model during the seedling, budding, and flowering-boll stages were 0.92, 0.97, and 0.95, respectively, and the model performance was superior to SG-SSA-BPNN. Therefore, the SG-1DCNN model was embedded into the detection system. Finally, a CCC intelligent detection system was developed using Python 3.12.3, MATLAB 2020b, and ENVI, and the system was subjected to application testing. The results showed that the average detection accuracy of the CCC intelligent detection system in the three stages was 98.522%, 99.132%, and 97.449%, respectively. Meanwhile, the average detection time for the samples is only 20.12 s. The research results can effectively solve the problem of detecting the nutritional status of cotton in the field environment, meet the real-time detection needs of the field environment, and provide solutions and technical support for the intelligent perception of crop production. Full article

The development of high-performance adsorbent materials is crucial for any sorption-based energy conversion process. In such a context, composite sorbent materials, although promising in terms of performance and stability, are often challenging to shape into complex geometries. Additive manufacturing, also known as 3D printing, has emerged as a powerful technique for fabricating intricate structures with tailored properties. In this paper, an innovative three-dimensional structure, constituted by zeolite as filler and sulfonated polyether ether ketone as matrix, was obtained using additive manufacturing technology, which is mainly suitable for sorption-based energy conversion processes. The lattice structure was tailored in order to optimize the synthesis procedure and material stability. The complex three-dimensional lattice structure was obtained without a metal or plastic reinforcement support. The composite structure was evaluated to assess its structural integrity using morphological analysis. Furthermore, the adsorption/desorption capacity was evaluated using water-vapor adsorption isobars at 11 mbar at equilibrium in the temperature range 30–120 °C, confirming good adsorption/desorption capacity. Full article

Traditional approaches in tunnel lighting have been directed toward the installation of appropriate luminaires in the intermediate and transitional sections with the simple objective of diminishing the effect of delayed visual accommodation during daylight hours. Such efforts run in parallel with the target of keeping the huge electrical use at the lowest level. Nevertheless, inadequate attention has been conceded to the interior areas, whose noticeable longitude in several instances, and subsequently the duration of occupancy of the users, can produce discomfort in the majority of the tunnel or underground passageway. It is in this region where the flicker effect presents a more remarkable impact. Although such effect is in fact uncomfortable, the strategies to eliminate it efficiently have not been developed in depth and the result is still deserving, especially in terms of sustainability. The reasons for this neglect, as well as some particularities and solutions, are exposed and discussed in the present article. Specifically, it is proved that the use of sunlight can be an adequate initiative and a positive energy input into design and retrofit tunnels capable of hampering or totally avoiding such unwanted effect. The innovative tunnel geometry explained in this manuscript is not cylindrical, and it is not based in revolution forms. Thus, it prevents the appearance of such unnerving visual effects, which compromise sustainability and endanger security. We are in the position to explain how the vector field generated by the normal to the points of the novel surface displayed remains non-parallel, ensuring appropriate diffusivity and, consequently, an even distribution of radiated energy. In the same manner, the notion of the tunnel is extended from a linear system to a veritable network of galleries, which can traverse in space bi- or even three-dimensionally. Accordingly, we will offer diverse instances of junctions and splices that further enhance the permeability into the terrain, augmenting the resilience capabilities of this disruptive technology. With all the former, a net reduction of costs reaching 25% can be easily expected with revenues. Full article

As semiconductor manufacturing advances into the angstrom-scale era characterized by three-dimensional integration, conventional metrology technologies face fundamental limitations regarding accuracy, speed, and non-destructiveness. Although optical spectroscopy has emerged as a prominent research focus, its application in complex manufacturing scenarios continues to confront significant technical barriers. This review establishes three concrete objectives: To categorize AI–optical spectroscopy integration paradigms spanning forward surrogate modeling, inverse prediction, physics-informed neural networks (PINNs), and multi-level architectures; to benchmark their efficacy against critical industrial metrology challenges including tool-to-tool (T2T) matching and high-aspect-ratio (HAR) structure characterization; and to identify unresolved bottlenecks for guiding next-generation intelligent semiconductor metrology. By categorically elaborating on the innovative applications of AI algorithms—such as forward surrogate models, inverse modeling techniques, physics-informed neural networks (PINNs), and multi-level network architectures—in optical spectroscopy, this work methodically assesses the implementation efficacy and limitations of each technical pathway. Through actual application case studies involving J-profiler software 5.0 and associated algorithms, this review validates the significant efficacy of AI technologies in addressing critical industrial challenges, including tool-to-tool (T2T) matching. The research demonstrates that the fusion of AI and optical spectroscopy delivers technological breakthroughs for semiconductor metrology; however, persistent challenges remain concerning data veracity, insufficient datasets, and cross-scale compatibility. Future research should prioritize enhancing model generalization capability, optimizing data acquisition and utilization strategies, and balancing algorithm real-time performance with accuracy, thereby catalyzing the transformation of semiconductor manufacturing towards an intelligence-driven advanced metrology paradigm. Full article

Physics-informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs’ development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence. The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid numerical-deep learning integration (5× speedup via domain decomposition) and second, constructing bidirectional theory-application mappings where convergence analysis (operator approximation theory) and generalization guarantees (Bayesian-physical hybrid frameworks) directly inform engineering implementations, as validated by $72\%$ cost reduction compared to FEM in high-dimensional spaces ($p<0.01,n=15$ benchmarks). Third, pioneering cross-domain knowledge transfer through application-specific architectures: TFE-PINN for turbulent flows ($5.12\pm 0.87\%$ error in NASA hypersonic tests), ReconPINN for medical imaging ($▵\mathrm{SSIM}=+0.18\pm 0.04$ on multi-institutional MRI), and SeisPINN for seismic systems ($0.52\pm 0.18$ km localization accuracy). We further present a technological roadmap highlighting three critical directions for PINN 2.0: neuro-symbolic, federated physics learning, and quantum-accelerated optimization. This work provides methodological guidelines and theoretical foundations for next-generation scientific machine learning systems. Full article

Point clouds acquired by mobile laser scanners (MLS) are widely used for generating as-built building information models (BIM), particularly in indoor construction environments and existing buildings. While MLS offers fast and efficient scanning through SLAM technology, its accuracy and precision remains lower than that of terrestrial laser scanners (TLS). This study investigates the potential to improve MLS-based as-built BIM accuracy by analyzing and utilizing error distribution patterns inherent in MLS point clouds. Based on the assumption that each MLS device exhibits consistent and unique error distribution patterns, an experiment was conducted using three MLS devices and TLS-derived reference data. The analysis employed iterative closest point (ICP) registration and cloud-to-mesh (C2M) distance measurements on mock-ups with closed shapes. The results revealed that error patterns were stable across scans and could be leveraged as correction factors. In other words, the results indicate that when using MLS for as-built BIM generation, robust fitting methods have limitations in obtaining realistic object dimensions, as they do not account for the unique error patterns present in MLS point clouds. The proposed method provides a simple and repeatable approach for enhancing MLS accuracy, contributing to improved dimensional reliability in MLS-driven BIM applications. Full article

The proliferation of electromagnetic wave applications has accentuated electromagnetic pollution concerns, highlighting the critical importance of electromagnetic wave absorbers (EMA). This study proposes innovative I-Wrapped Package Lattice electromagnetic wave absorbers (IWP–EMA) based on the triply periodic minimal surface (TPMS) lattice structure. Through a rational design of porous gradient structures, broadband wave absorption was achieved while maintaining lightweight characteristics and mechanical robustness. The optimized three-dimensional configuration features a 20 mm thick gradient structure with a progressive relative density transition from 10% to 30%. Under normal incidence conditions, this gradient IWP–EMA basically achieves broadband absorption with a reflection loss below −10 dB across the 2–40 GHz frequency band, with absorption peaks below −19 dB, demonstrating good impedance-matching characteristics. Additionally, due to the complex interactions of electromagnetic waves within the structure, the proposed IWP–EMA achieves a wide-angle absorption range of 70° under Transverse Electric (TE) polarization and 70° under Transverse Magnetic (TM) polarization. The synergistic integration of the TPMS design and additive manufacturing technology employed in this study significantly expands the design space and application potential of electromagnetic absorption structures. Full article

The research presented in this paper focuses on the utilization of 3D printing technology in the design and manufacture of a prosthetic hand, equipped with a digit replicator. The subject of this study was a young man who had undergone the amputation of two fingers on his right hand. The electronic control of the movement of the finger copy was developed using Arduino language. A concept and outline drawings were developed in ProCreate. Three-dimensional scan of the hand and forearm was made using an EinScan PRO HD SHINING 3D scanner. Using CAD software—Autodesk Inventor and Autodesk Meshmixer, the prosthesis was designed. Printing was carried out on a 3D printer of the i3 MK3 and MK3+ series using a PLA (polylactic acid) filament. It was determined that PLA is an optimal material for printing, as it is considered to be safe for future patients’ skin. Work on the electronic circuitry started in Autodesk TinkerCad simulation software, allowing the code to be verified and ensuring the safety of the control system. The prosthesis’s design demonstrates the potential to reach as many people in need as possible by using readily available, low-cost, and easy-to-use components. Full article

The increasing incidence of oil contamination in many aquatic ecosystems, particularly in oil-rich regions such as Qatar, poses significant threats to marine life and human activities. Our study addresses the critical need for effective and eco-friendly oil-water separation techniques, focusing on developing graphene and chitosan-based three-dimensional (3D) polymeric sponges. These materials have demonstrated potential due to their high porosity and surface area, which can be enhanced through surface treatment to improve hydrophobicity and oleophilicity. This study introduces a new technique dependent on the optimization of the graphene oxide (GO) concentration within the composite sponge to achieve a superior oil uptake capacity (51.4 g oil/g sponge at 3% GO), and the detailed characterization of the material’s performance in separating heavy oil-water emulsions. Our study seeks to answer key questions regarding the performance of these modified sponges and their scalability for industrial applications. This research directly aligns with Qatar’s environmental goals and develops sustainable oil-water separation technologies. It addresses the pressing challenges of oil spills, ultimately contributing to improved marine ecosystem protection and efficient resource recovery. Full article

The digital economy presents multiple challenges to the promotion of green technologies, including behavioral uncertainty among firms, heterogeneous technological choices, and disparities in policy incentive strength. This study develops a tripartite evolutionary game model encompassing government, production enterprises, and technology suppliers to systematically explore the strategic evolution mechanisms underlying green technology adoption. A three-dimensional nonlinear dynamic system is constructed using replicator dynamics, and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is applied to identify key cost and benefit parameters for firms. Simulation results exhibit a strong match between the estimated parameters and simulated data, highlighting the model’s identifiability and explanatory capacity. In addition, the stability of eight pure strategy equilibrium points is examined through Jacobian analysis, revealing the evolutionary trajectories and local stability features across various strategic configurations. These findings offer theoretical guidance for optimizing green policy design and identifying behavioral pathways, while establishing a foundation for data-driven modeling of dynamic evolutionary processes. Full article

Advanced three-dimensional extended reality (XR) technologies are highly suitable for cultural heritage research and education. XR tools enable the creation of realistic virtual or augmented reality applications for curating and disseminating information about cultural artifacts and sites. Developing XR applications for cultural heritage requires interdisciplinary collaboration involving strong teamwork and soft skills to manage user requirements, system specifications, and design cycles. Given the diverse end-users, achieving high precision, accuracy, and efficiency in information management and user experience is crucial. Human–computer interaction (HCI) design and evaluation methods are essential for ensuring usability and return on investment. This article presents ten case studies of cultural heritage software projects, illustrating the interdisciplinary work between computer science and HCI design. Students from institutions such as the State University of New York (USA), Glasgow School of Art (UK), University of Granada (Spain), University of Málaga (Spain), Duy Tan University (Vietnam), Imperial College London (UK), Research University Institute of Communication & Computer Systems (Greece), Technical University of Košice (Slovakia), and Indiana University (USA) contributed to creating, assessing, and improving the usability of these diverse cultural heritage applications. The results include a structured typology of CH XR application scenarios, detailed insights into design and evaluation practices across ten international use cases, and a development framework that supports interdisciplinary collaboration and stakeholder integration in phygital cultural heritage projects. Full article

Computational fluid dynamics (CFD) has aided the development, design, and analysis of hypersonic airbreathing propulsion technologies, such as scramjets. The complex flow field in a scramjet isolator has been the subject of intense interest and study for several decades. Many features of this flow field also occur in supersonic wind-tunnel nozzles and diffusers. Computational analysis of these topics has frequently provided immense insight into the actual functionality and performance. Research presented in this work supports scientific investigation and understanding of a less-researched topic, which is shock–shock interference and interaction with the boundary layer in supersonic internal flows, as well as the passive control of its adverse effects to prevent the onset of unstart in a scramjet isolator. This computational investigation is conducted on a backpressured isolator and a modified three-dimensional shock-tube to represent a scramjet isolator with ram effects provided by high-pressure gas and high-speed flow provided by a supersonic inflow. Computational results for the backpressured isolator have been validated against available measured time-averaged wall pressure data. The modified shock-tube provided an opportunity to study the shock–shock interference and shock–boundary-layer interaction effects that would occur in a scramjet isolator or a ram-accelerator when the high-speed flow from the inlet interacted with the shock produced due to the combustor pressure traveling and meeting in the isolator. An assessment of wall cooling effects on these phenomena is presented for both the backpressured isolator and the modified shock-tube. Full article

The ability to model the earliest stages of human embryonic development in vitro using pluripotent stem cells offers researchers new ways to understand and interrogate the intricacies of implantation. It also raises important ethical and regulatory considerations, both those common to research involving human embryos, as well as those unique to stem cell-based embryo and endometrial models. This review examines the underpinning scientific discoveries that have led to the development of this rapidly expanding area of research, and how three-dimensional embryo models could be employed in advancing assisted reproductive technologies and understanding implantation failure. Importantly, we also discuss the ethical and legal implications and explore various governance models that have been proposed to foster responsibility and innovation in this area of research. Given the heightened interest in the scientific community on this topic, we finish on the question of how and when to involve the public in the development of this technology and its regulation. Full article

In recent years, pelvic organ prolapse (POP) cases have been rising, affecting women’s quality of life. Synthetic surgical transvaginal meshes used for POP treatment were withdrawn from the United States market in 2019 due to high risks, including infection, vaginal mesh erosion, and POP reoccurrence. Biodegradable mesh implants with three-dimensional printing technology have emerged as an innovative alternative. In this study, polycaprolactone (PCL) meshes for POP repair were fabricated using melt electrospinning writing (MEW) and mechanically evaluated through uniaxial tensile tests. Following this, they were coated with antibiotics—azithromycin, gentamicin sulfate, and ciprofloxacin—commonly used for genitourinary tract infections. Zone inhibition and biofilm assays evaluated antibiotic effectiveness in preventing mesh infections by Escherichia coli, and methicillin-susceptible (MSSA) and methicillin-resistant (MRSA) Staphylococcus aureus. The meshes presented a mechanical behavior closer to vaginal tissue than commercially available meshes. Fourier transform infrared analysis confirmed antibiotic incorporation. Ciprofloxacin demonstrated antibacterial activity against MRSA, with a 92% reduction in metabolic activity and a 99% biomass reduction. Gentamicin and ciprofloxacin displayed inhibitory activity against MSSA and E. coli. Scanning electron microscopy images support these conclusions. This methodology may offer a more effective, patient-friendly solution for POP repair, improving healing and the quality of life for affected women. Full article

Electromagnetic articulography is a technique developed for recording three-dimensional movements. It is based on magnetic induction, where small currents are induced in miniature receiver coils acting as motion sensors by means of electromagnetic fields generated by transmitter coils. This technology has been applied in dental research to record mandibular movements during mastication, Posselt’s envelope of motion, and micromovements of dental prostheses. The AG501 electromagnetic articulograph (Carstens Medizinelektronik GmbH, Bovenden, Germany) provides a Head Correction (HC) procedure to eliminate head movement, which requires the reference sensors to be firmly attached to the subject’s head. If the sensors shift during the recordings, it becomes necessary to reposition them and repeat the head correction procedure. The aim of this study was to develop a 3D-printed helmet to securely fix the reference sensors to the head of a subject in the context of performing a series of recordings involving the mastication of 36 foods and the execution of Posselt’s envelope of motion. The number of HCs required was recorded for a group using the helmet and for a control group in which the sensors were attached to the subject’s head using tissue adhesive. A total of 29 recordings were conducted with and without the helmet. Without the helmet 44 HCs were required; on the other hand, with the helmet 36 HCs were required. On average, 1.5 HCs were required per session without the helmet and 1.2 HCs with the helmet, showing a non-significant difference (p < 0.05). A reduction in the number of HCs required per session was observed. However, more than one HC was still needed to complete a session. This could be addressed in future research by designing a series of helmets that adapt to different head sizes. Full article